Estimating Seasonal Behavior States from Bio-logging Sensor Data
The seasonal timing of key, annual life history events is an important component of many species' ecology. Seasonal periods important to marine mammals often do not align well with typical labels (i.e., spring, summer, winter, fall). The timing of key life history events is well documented only...
| Main Authors: | , , , , , |
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| Format: | Conference Object |
| Language: | unknown |
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figshare
2015
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.6084/m9.figshare.2057928.v1 https://figshare.com/articles/presentation/Estimating_Seasonal_Behavior_States_from_Bio_logging_Sensor_Data/2057928/1 |
| Summary: | The seasonal timing of key, annual life history events is an important component of many species' ecology. Seasonal periods important to marine mammals often do not align well with typical labels (i.e., spring, summer, winter, fall). The timing of key life history events is well documented only for species found in accessible rookeries or breeding areas. Our knowledge of seasonal timing for species widely dispersed in inaccessible or remote habitats is poor. Here, we employed data from biologging sensors and new statistical modeling to identify and estimate timing of seasonal states for adult bearded seals (n=7) captured in Kotzebue Sound, Alaska. These animals provide an initial, small dataset we can work with before expanding to include ribbon and spotted seals in future iterations. Each of these seals is reliant on the seasonal sea ice for pupping, nursing, breeding and molting and these seasons can be characterized by more time spent hauled out on ice, by changes in dive behavior, and by changes in large-scale movement. We are especially interested in the pupping-breeding-molting season, but also use this approach to identify seasonal structure in the non-breeding period. Seasonal periods were treated as separate behavior states that correspond to a hidden Markov process. Hidden Markov models (HMM) are commonly used to estimate behavior states (e.g., foraging, resting, transit) from telemetry data. Typical HMMs, however, have no temporal memory of state assignments and would likely not capture seasonal level states. To address this, we applied a hidden semi-Markov model. Dive and haul-out behavior from biologgers were used to estimate these states. The timing and extent of sea ice in the Bering Sea is predicted to change dramatically over the next 50 years and we anticipate bearded, ribbon, and spotted seals might adjust the timing of these life history events in response to those changes. |
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